{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xieyipeng/Documents/MachineLearning/wuenda_py/course2/do_not_src/course2_week1_2/reg_utils.py:61: SyntaxWarning: assertion is always true, perhaps remove parentheses?\n",
      "  assert(parameters['W' + str(l)].shape == layer_dims[l], layer_dims[l-1])\n",
      "/home/xieyipeng/Documents/MachineLearning/wuenda_py/course2/do_not_src/course2_week1_2/reg_utils.py:62: SyntaxWarning: assertion is always true, perhaps remove parentheses?\n",
      "  assert(parameters['W' + str(l)].shape == layer_dims[l], 1)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "import sklearn.datasets\n",
    "from do_not_src.course2_week1_2 import init_utils   #第一部分，初始化\n",
    "from do_not_src.course2_week1_2 import reg_utils    #第二部分，正则化\n",
    "from do_not_src.course2_week1_2 import gc_utils     #第三部分，梯度校验\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (7.0, 4.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_X, train_Y, test_X, test_Y = init_utils.load_dataset(is_plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model(X,Y,learning_rate=0.01,num_iterations=15000,print_cost=True,initialization=\"he\",is_polt=True):\n",
    "    \"\"\"\n",
    "    实现一个三层的神经网络：LINEAR ->RELU -> LINEAR -> RELU -> LINEAR -> SIGMOID\n",
    "    \n",
    "    参数：\n",
    "        X - 输入的数据，维度为(2, 要训练/测试的数量)\n",
    "        Y - 标签，【0 | 1】，维度为(1，对应的是输入的数据的标签)\n",
    "        learning_rate - 学习速率\n",
    "        num_iterations - 迭代的次数\n",
    "        print_cost - 是否打印成本值，每迭代1000次打印一次\n",
    "        initialization - 字符串类型，初始化的类型【\"zeros\" | \"random\" | \"he\"】\n",
    "        is_polt - 是否绘制梯度下降的曲线图\n",
    "    返回\n",
    "        parameters - 学习后的参数\n",
    "    \"\"\"\n",
    "    grads = {}\n",
    "    costs = []\n",
    "    m = X.shape[1]\n",
    "    layers_dims = [X.shape[0],10,5,1]\n",
    "    \n",
    "    #选择初始化参数的类型\n",
    "    if initialization == \"zeros\":\n",
    "        parameters = initialize_parameters_zeros(layers_dims)\n",
    "    elif initialization == \"random\":\n",
    "        parameters = initialize_parameters_random(layers_dims)\n",
    "    elif initialization == \"he\":\n",
    "        parameters = initialize_parameters_he(layers_dims)\n",
    "    else : \n",
    "        print(\"错误的初始化参数！程序退出\")\n",
    "        exit\n",
    "    \n",
    "    #开始学习\n",
    "    for i in range(0,num_iterations):\n",
    "        #前向传播\n",
    "        a3 , cache = init_utils.forward_propagation(X,parameters)\n",
    "        \n",
    "        #计算成本        \n",
    "        cost = init_utils.compute_loss(a3,Y)\n",
    "        \n",
    "        #反向传播\n",
    "        grads = init_utils.backward_propagation(X,Y,cache)\n",
    "        \n",
    "        #更新参数\n",
    "        parameters = init_utils.update_parameters(parameters,grads,learning_rate)\n",
    "        \n",
    "        #记录成本\n",
    "        if i % 1000 == 0:\n",
    "            costs.append(cost)\n",
    "            #打印成本\n",
    "            if print_cost:\n",
    "                print(\"第\" + str(i) + \"次迭代，成本值为：\" + str(cost))\n",
    "        \n",
    "    \n",
    "    #学习完毕，绘制成本曲线\n",
    "    if is_polt:\n",
    "        plt.plot(costs)\n",
    "        plt.ylabel('cost')\n",
    "        plt.xlabel('iterations (per hundreds)')\n",
    "        plt.title(\"Learning rate =\" + str(learning_rate))\n",
    "        plt.show()\n",
    "    \n",
    "    #返回学习完毕后的参数\n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize_parameters_zeros(layers_dims):\n",
    "    \"\"\"\n",
    "    将模型的参数全部设置为0\n",
    "    \n",
    "    参数：\n",
    "        layers_dims - 列表，模型的层数和对应每一层的节点的数量\n",
    "    返回\n",
    "        parameters - 包含了所有W和b的字典\n",
    "            W1 - 权重矩阵，维度为（layers_dims[1], layers_dims[0]）\n",
    "            b1 - 偏置向量，维度为（layers_dims[1],1）\n",
    "            ···\n",
    "            WL - 权重矩阵，维度为（layers_dims[L], layers_dims[L -1]）\n",
    "            bL - 偏置向量，维度为（layers_dims[L],1）\n",
    "    \"\"\"\n",
    "    parameters = {}\n",
    "    \n",
    "    L = len(layers_dims) #网络层数\n",
    "    \n",
    "    for l in range(1,L):\n",
    "        parameters[\"W\" + str(l)] = np.zeros((layers_dims[l],layers_dims[l-1]))\n",
    "        parameters[\"b\" + str(l)] = np.zeros((layers_dims[l],1))\n",
    "        \n",
    "        #使用断言确保我的数据格式是正确的\n",
    "        assert(parameters[\"W\" + str(l)].shape == (layers_dims[l],layers_dims[l-1]))\n",
    "        assert(parameters[\"b\" + str(l)].shape == (layers_dims[l],1))\n",
    "        \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 初始化为0："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[0. 0. 0.]\n",
      " [0. 0. 0.]]\n",
      "b1 = [[0.]\n",
      " [0.]]\n",
      "W2 = [[0. 0.]]\n",
      "b2 = [[0.]]\n"
     ]
    }
   ],
   "source": [
    "parameters = initialize_parameters_zeros([3,2,1])\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第0次迭代，成本值为：0.6931471805599453\n",
      "第1000次迭代，成本值为：0.6931471805599453\n",
      "第2000次迭代，成本值为：0.6931471805599453\n",
      "第3000次迭代，成本值为：0.6931471805599453\n",
      "第4000次迭代，成本值为：0.6931471805599453\n",
      "第5000次迭代，成本值为：0.6931471805599453\n",
      "第6000次迭代，成本值为：0.6931471805599453\n",
      "第7000次迭代，成本值为：0.6931471805599453\n",
      "第8000次迭代，成本值为：0.6931471805599453\n",
      "第9000次迭代，成本值为：0.6931471805599453\n",
      "第10000次迭代，成本值为：0.6931471805599455\n",
      "第11000次迭代，成本值为：0.6931471805599453\n",
      "第12000次迭代，成本值为：0.6931471805599453\n",
      "第13000次迭代，成本值为：0.6931471805599453\n",
      "第14000次迭代，成本值为：0.6931471805599453\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "parameters = model(train_X, train_Y, initialization = \"zeros\",is_polt=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集:\n",
      "Accuracy: 0.5\n",
      "测试集:\n",
      "Accuracy: 0.5\n"
     ]
    }
   ],
   "source": [
    "print (\"训练集:\")\n",
    "predictions_train = init_utils.predict(train_X, train_Y, parameters)\n",
    "print (\"测试集:\")\n",
    "predictions_test = init_utils.predict(test_X, test_Y, parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predictions_train = [[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0]]\n",
      "predictions_test = [[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"predictions_train = \" + str(predictions_train))\n",
    "print(\"predictions_test = \" + str(predictions_test))\n",
    "\n",
    "plt.title(\"Model with Zeros initialization\")\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-1.5, 1.5])\n",
    "axes.set_ylim([-1.5, 1.5])\n",
    "init_utils.plot_decision_boundary(lambda x: init_utils.predict_dec(parameters, x.T), train_X, train_Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 随机初始化："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize_parameters_random(layers_dims):\n",
    "    \"\"\"\n",
    "    参数：\n",
    "        layers_dims - 列表，模型的层数和对应每一层的节点的数量\n",
    "    返回\n",
    "        parameters - 包含了所有W和b的字典\n",
    "            W1 - 权重矩阵，维度为（layers_dims[1], layers_dims[0]）\n",
    "            b1 - 偏置向量，维度为（layers_dims[1],1）\n",
    "            ···\n",
    "            WL - 权重矩阵，维度为（layers_dims[L], layers_dims[L -1]）\n",
    "            b1 - 偏置向量，维度为（layers_dims[L],1）\n",
    "    \"\"\"\n",
    "    \n",
    "    np.random.seed(3)               # 指定随机种子\n",
    "    parameters = {}\n",
    "    L = len(layers_dims)            # 层数\n",
    "    \n",
    "    for l in range(1, L):\n",
    "        parameters['W' + str(l)] = np.random.randn(layers_dims[l], layers_dims[l - 1]) * 10 #使用10倍缩放\n",
    "        parameters['b' + str(l)] = np.zeros((layers_dims[l], 1))\n",
    "        \n",
    "        #使用断言确保我的数据格式是正确的\n",
    "        assert(parameters[\"W\" + str(l)].shape == (layers_dims[l],layers_dims[l-1]))\n",
    "        assert(parameters[\"b\" + str(l)].shape == (layers_dims[l],1))\n",
    "        \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[ 17.88628473   4.36509851   0.96497468]\n",
      " [-18.63492703  -2.77388203  -3.54758979]]\n",
      "b1 = [[0.]\n",
      " [0.]]\n",
      "W2 = [[-0.82741481 -6.27000677]]\n",
      "b2 = [[0.]]\n"
     ]
    }
   ],
   "source": [
    "parameters = initialize_parameters_random([3, 2, 1])\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xieyipeng/Documents/MachineLearning/wuenda_py/course2/do_not_src/course2_week1_2/init_utils.py:50: RuntimeWarning: divide by zero encountered in log\n",
      "  logprobs = np.multiply(-np.log(a3),Y) + np.multiply(-np.log(1 - a3), 1 - Y)\n",
      "/home/xieyipeng/Documents/MachineLearning/wuenda_py/course2/do_not_src/course2_week1_2/init_utils.py:50: RuntimeWarning: invalid value encountered in multiply\n",
      "  logprobs = np.multiply(-np.log(a3),Y) + np.multiply(-np.log(1 - a3), 1 - Y)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第0次迭代，成本值为：inf\n",
      "第1000次迭代，成本值为：0.6242434241539614\n",
      "第2000次迭代，成本值为：0.5978811277755388\n",
      "第3000次迭代，成本值为：0.5636242569764779\n",
      "第4000次迭代，成本值为：0.5500958254523324\n",
      "第5000次迭代，成本值为：0.544339206192789\n",
      "第6000次迭代，成本值为：0.5373584514307651\n",
      "第7000次迭代，成本值为：0.469574666760224\n",
      "第8000次迭代，成本值为：0.39766324943219844\n",
      "第9000次迭代，成本值为：0.3934423376823982\n",
      "第10000次迭代，成本值为：0.3920158992175907\n",
      "第11000次迭代，成本值为：0.38913979237487845\n",
      "第12000次迭代，成本值为：0.3861261344766218\n",
      "第13000次迭代，成本值为：0.3849694511273874\n",
      "第14000次迭代，成本值为：0.3827489017191917\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集：\n",
      "Accuracy: 0.83\n",
      "测试集：\n",
      "Accuracy: 0.86\n",
      "[[1 0 1 1 0 0 1 1 1 1 1 0 1 0 0 1 0 1 1 0 0 0 1 0 1 1 1 1 1 1 0 1 1 0 0 1\n",
      "  1 1 1 1 1 1 1 0 1 1 1 1 0 1 0 1 1 1 1 0 0 1 1 1 1 0 1 1 0 1 0 1 1 1 1 0\n",
      "  0 0 0 0 1 0 1 0 1 1 1 0 0 1 1 1 1 1 1 0 0 1 1 1 0 1 1 0 1 0 1 1 0 1 1 0\n",
      "  1 0 1 1 0 0 1 0 0 1 1 0 1 1 1 0 1 0 0 1 0 1 1 1 1 1 1 1 0 1 1 0 0 1 1 0\n",
      "  0 0 1 0 1 0 1 0 1 1 1 0 0 1 1 1 1 0 1 1 0 1 0 1 1 0 1 0 1 1 1 1 0 1 1 1\n",
      "  1 0 1 0 1 0 1 1 1 1 0 1 1 0 1 1 0 1 1 0 1 0 1 1 1 0 1 1 1 0 1 0 1 0 0 1\n",
      "  0 1 1 0 1 1 0 1 1 0 1 1 1 0 1 1 1 1 0 1 0 0 1 1 0 1 1 1 0 0 0 1 1 0 1 1\n",
      "  1 1 0 1 1 0 1 1 1 0 0 1 0 0 0 1 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 1 1 1\n",
      "  1 1 1 1 0 0 0 1 1 1 1 0]]\n",
      "[[1 1 1 1 0 1 0 1 1 0 1 1 1 0 0 0 0 1 0 1 0 0 1 0 1 0 1 1 1 1 1 0 0 0 0 1\n",
      "  0 1 1 0 0 1 1 1 1 1 0 1 1 1 0 1 0 1 1 0 1 0 1 0 1 1 1 1 1 1 1 1 1 0 1 0\n",
      "  1 1 1 1 1 0 1 0 0 1 0 0 0 1 1 0 1 1 0 0 0 1 1 0 1 1 0 0]]\n"
     ]
    }
   ],
   "source": [
    "parameters = model(train_X, train_Y, initialization = \"random\",is_polt=True)\n",
    "print(\"训练集：\")\n",
    "predictions_train = init_utils.predict(train_X, train_Y, parameters)\n",
    "print(\"测试集：\")\n",
    "predictions_test = init_utils.predict(test_X, test_Y, parameters)\n",
    "\n",
    "print(predictions_train)\n",
    "print(predictions_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Model with large random initialization\")\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-1.5, 1.5])\n",
    "axes.set_ylim([-1.5, 1.5])\n",
    "init_utils.plot_decision_boundary(lambda x: init_utils.predict_dec(parameters, x.T), train_X, train_Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 抑梯度异常初始化:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize_parameters_he(layers_dims):\n",
    "    \"\"\"\n",
    "    参数：\n",
    "        layers_dims - 列表，模型的层数和对应每一层的节点的数量\n",
    "    返回\n",
    "        parameters - 包含了所有W和b的字典\n",
    "            W1 - 权重矩阵，维度为（layers_dims[1], layers_dims[0]）\n",
    "            b1 - 偏置向量，维度为（layers_dims[1],1）\n",
    "            ···\n",
    "            WL - 权重矩阵，维度为（layers_dims[L], layers_dims[L -1]）\n",
    "            b1 - 偏置向量，维度为（layers_dims[L],1）\n",
    "    \"\"\"\n",
    "    \n",
    "    np.random.seed(3)               # 指定随机种子\n",
    "    parameters = {}\n",
    "    L = len(layers_dims)            # 层数\n",
    "    \n",
    "    for l in range(1, L):\n",
    "        parameters['W' + str(l)] = np.random.randn(layers_dims[l], layers_dims[l - 1]) * np.sqrt(2 / layers_dims[l - 1])\n",
    "        parameters['b' + str(l)] = np.zeros((layers_dims[l], 1))\n",
    "        \n",
    "        #使用断言确保我的数据格式是正确的\n",
    "        assert(parameters[\"W\" + str(l)].shape == (layers_dims[l],layers_dims[l-1]))\n",
    "        assert(parameters[\"b\" + str(l)].shape == (layers_dims[l],1))\n",
    "        \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[ 1.78862847  0.43650985]\n",
      " [ 0.09649747 -1.8634927 ]\n",
      " [-0.2773882  -0.35475898]\n",
      " [-0.08274148 -0.62700068]]\n",
      "b1 = [[0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]]\n",
      "W2 = [[-0.03098412 -0.33744411 -0.92904268  0.62552248]]\n",
      "b2 = [[0.]]\n"
     ]
    }
   ],
   "source": [
    "parameters = initialize_parameters_he([2, 4, 1])\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第0次迭代，成本值为：0.8830537463419761\n",
      "第1000次迭代，成本值为：0.6879825919728063\n",
      "第2000次迭代，成本值为：0.6751286264523371\n",
      "第3000次迭代，成本值为：0.6526117768893807\n",
      "第4000次迭代，成本值为：0.6082958970572938\n",
      "第5000次迭代，成本值为：0.5304944491717495\n",
      "第6000次迭代，成本值为：0.4138645817071794\n",
      "第7000次迭代，成本值为：0.3117803464844441\n",
      "第8000次迭代，成本值为：0.23696215330322562\n",
      "第9000次迭代，成本值为：0.18597287209206834\n",
      "第10000次迭代，成本值为：0.15015556280371806\n",
      "第11000次迭代，成本值为：0.12325079292273546\n",
      "第12000次迭代，成本值为：0.09917746546525934\n",
      "第13000次迭代，成本值为：0.08457055954024278\n",
      "第14000次迭代，成本值为：0.07357895962677369\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集:\n",
      "Accuracy: 0.9933333333333333\n",
      "测试集:\n",
      "Accuracy: 0.96\n"
     ]
    }
   ],
   "source": [
    "parameters = model(train_X, train_Y, initialization = \"he\",is_polt=True)\n",
    "print(\"训练集:\")\n",
    "predictions_train = init_utils.predict(train_X, train_Y, parameters)\n",
    "print(\"测试集:\")\n",
    "init_utils.predictions_test = init_utils.predict(test_X, test_Y, parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Model with He initialization\")\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-1.5, 1.5])\n",
    "axes.set_ylim([-1.5, 1.5])\n",
    "init_utils.plot_decision_boundary(lambda x: init_utils.predict_dec(parameters, x.T), train_X, train_Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 正则化模型："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_X, train_Y, test_X, test_Y = reg_utils.load_2D_dataset(is_plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model(X,Y,learning_rate=0.3,num_iterations=30000,print_cost=True,is_plot=True,lambd=0,keep_prob=1):\n",
    "    \"\"\"\n",
    "    实现一个三层的神经网络：LINEAR ->RELU -> LINEAR -> RELU -> LINEAR -> SIGMOID\n",
    "    \n",
    "    参数：\n",
    "        X - 输入的数据，维度为(2, 要训练/测试的数量)\n",
    "        Y - 标签，【0(蓝色) | 1(红色)】，维度为(1，对应的是输入的数据的标签)\n",
    "        learning_rate - 学习速率\n",
    "        num_iterations - 迭代的次数\n",
    "        print_cost - 是否打印成本值，每迭代10000次打印一次，但是每1000次记录一个成本值\n",
    "        is_polt - 是否绘制梯度下降的曲线图\n",
    "        lambd - 正则化的超参数，实数\n",
    "        keep_prob - 随机删除节点的概率\n",
    "    返回\n",
    "        parameters - 学习后的参数\n",
    "    \"\"\"\n",
    "    grads = {}\n",
    "    costs = []\n",
    "    m = X.shape[1]\n",
    "    layers_dims = [X.shape[0],20,3,1]\n",
    "    \n",
    "    #初始化参数\n",
    "    parameters = reg_utils.initialize_parameters(layers_dims)\n",
    "    \n",
    "    #开始学习\n",
    "    for i in range(0,num_iterations):\n",
    "        #前向传播\n",
    "        ##是否随机删除节点\n",
    "        if keep_prob == 1:\n",
    "            ###不随机删除节点\n",
    "            a3 , cache = reg_utils.forward_propagation(X,parameters)\n",
    "        elif keep_prob < 1:\n",
    "            ###随机删除节点\n",
    "            a3 , cache = forward_propagation_with_dropout(X,parameters,keep_prob)\n",
    "        else:\n",
    "            print(\"keep_prob参数错误！程序退出。\")\n",
    "            exit\n",
    "        \n",
    "        #计算成本\n",
    "        ## 是否使用二范数\n",
    "        if lambd == 0:\n",
    "            ###不使用L2正则化\n",
    "            cost = reg_utils.compute_cost(a3,Y)\n",
    "        else:\n",
    "            ###使用L2正则化\n",
    "            cost = compute_cost_with_regularization(a3,Y,parameters,lambd)\n",
    "        \n",
    "        #反向传播\n",
    "        ##可以同时使用L2正则化和随机删除节点，但是本次实验不同时使用。\n",
    "        assert(lambd == 0  or keep_prob ==1)\n",
    "        \n",
    "        ##两个参数的使用情况\n",
    "        if (lambd == 0 and keep_prob == 1):\n",
    "            ### 不使用L2正则化和不使用随机删除节点\n",
    "            grads = reg_utils.backward_propagation(X,Y,cache)\n",
    "        elif lambd != 0:\n",
    "            ### 使用L2正则化，不使用随机删除节点\n",
    "            grads = backward_propagation_with_regularization(X, Y, cache, lambd)\n",
    "        elif keep_prob < 1:\n",
    "            ### 使用随机删除节点，不使用L2正则化\n",
    "            grads = backward_propagation_with_dropout(X, Y, cache, keep_prob)\n",
    "        \n",
    "        #更新参数\n",
    "        parameters = reg_utils.update_parameters(parameters, grads, learning_rate)\n",
    "        \n",
    "        #记录并打印成本\n",
    "        if i % 1000 == 0:\n",
    "            ## 记录成本\n",
    "            costs.append(cost)\n",
    "            if (print_cost and i % 10000 == 0):\n",
    "                #打印成本\n",
    "                print(\"第\" + str(i) + \"次迭代，成本值为：\" + str(cost))\n",
    "        \n",
    "    #是否绘制成本曲线图\n",
    "    if is_plot:\n",
    "        plt.plot(costs)\n",
    "        plt.ylabel('cost')\n",
    "        plt.xlabel('iterations (x1,000)')\n",
    "        plt.title(\"Learning rate =\" + str(learning_rate))\n",
    "        plt.show()\n",
    "    \n",
    "    #返回学习后的参数\n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 不使用正则化："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第0次迭代，成本值为：0.6557412523481002\n",
      "第10000次迭代，成本值为：0.16329987525724213\n",
      "第20000次迭代，成本值为：0.13851642423265018\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集:\n",
      "Accuracy: 0.9478672985781991\n",
      "测试集:\n",
      "Accuracy: 0.915\n"
     ]
    }
   ],
   "source": [
    "parameters = model(train_X, train_Y,is_plot=True)\n",
    "print(\"训练集:\")\n",
    "predictions_train = reg_utils.predict(train_X, train_Y, parameters)\n",
    "print(\"测试集:\")\n",
    "predictions_test = reg_utils.predict(test_X, test_Y, parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Model without regularization\")\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-0.75,0.40])\n",
    "axes.set_ylim([-0.75,0.65])\n",
    "reg_utils.plot_decision_boundary(lambda x: reg_utils.predict_dec(parameters, x.T), train_X, train_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_cost_with_regularization(A3,Y,parameters,lambd):\n",
    "    \"\"\"\n",
    "    实现公式2的L2正则化计算成本\n",
    "    \n",
    "    参数：\n",
    "        A3 - 正向传播的输出结果，维度为（输出节点数量，训练/测试的数量）\n",
    "        Y - 标签向量，与数据一一对应，维度为(输出节点数量，训练/测试的数量)\n",
    "        parameters - 包含模型学习后的参数的字典\n",
    "    返回：\n",
    "        cost - 使用公式2计算出来的正则化损失的值\n",
    "    \n",
    "    \"\"\"\n",
    "    m = Y.shape[1]\n",
    "    W1 = parameters[\"W1\"]\n",
    "    W2 = parameters[\"W2\"]\n",
    "    W3 = parameters[\"W3\"]\n",
    "    \n",
    "    cross_entropy_cost = reg_utils.compute_cost(A3,Y)\n",
    "    \n",
    "    L2_regularization_cost = lambd * (np.sum(np.square(W1)) + np.sum(np.square(W2))  + np.sum(np.square(W3))) / (2 * m)\n",
    "    \n",
    "    cost = cross_entropy_cost + L2_regularization_cost\n",
    "    \n",
    "    return cost\n",
    "\n",
    "#当然，因为改变了成本函数，我们也必须改变向后传播的函数， 所有的梯度都必须根据这个新的成本值来计算。\n",
    "\n",
    "def backward_propagation_with_regularization(X, Y, cache, lambd):\n",
    "    \"\"\"\n",
    "    实现我们添加了L2正则化的模型的后向传播。\n",
    "    \n",
    "    参数：\n",
    "        X - 输入数据集，维度为（输入节点数量，数据集里面的数量）\n",
    "        Y - 标签，维度为（输出节点数量，数据集里面的数量）\n",
    "        cache - 来自forward_propagation（）的cache输出\n",
    "        lambda - regularization超参数，实数\n",
    "    \n",
    "    返回：\n",
    "        gradients - 一个包含了每个参数、激活值和预激活值变量的梯度的字典\n",
    "    \"\"\"\n",
    "    \n",
    "    m = X.shape[1]\n",
    "    \n",
    "    (Z1, A1, W1, b1, Z2, A2, W2, b2, Z3, A3, W3, b3) = cache\n",
    "    \n",
    "    dZ3 = A3 - Y\n",
    "    \n",
    "    dW3 = (1 / m) * np.dot(dZ3,A2.T) + ((lambd * W3) / m )\n",
    "    db3 = (1 / m) * np.sum(dZ3,axis=1,keepdims=True)\n",
    "    \n",
    "    dA2 = np.dot(W3.T,dZ3)\n",
    "    dZ2 = np.multiply(dA2,np.int64(A2 > 0))\n",
    "    dW2 = (1 / m) * np.dot(dZ2,A1.T) + ((lambd * W2) / m)\n",
    "    db2 = (1 / m) * np.sum(dZ2,axis=1,keepdims=True)\n",
    "    \n",
    "    dA1 = np.dot(W2.T,dZ2)\n",
    "    dZ1 = np.multiply(dA1,np.int64(A1 > 0))\n",
    "    dW1 = (1 / m) * np.dot(dZ1,X.T) + ((lambd * W1) / m)\n",
    "    db1 = (1 / m) * np.sum(dZ1,axis=1,keepdims=True)\n",
    "    \n",
    "    gradients = {\"dZ3\": dZ3, \"dW3\": dW3, \"db3\": db3, \"dA2\": dA2,\n",
    "                 \"dZ2\": dZ2, \"dW2\": dW2, \"db2\": db2, \"dA1\": dA1, \n",
    "                 \"dZ1\": dZ1, \"dW1\": dW1, \"db1\": db1}\n",
    "    \n",
    "    return gradients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第0次迭代，成本值为：0.6974484493131264\n",
      "第10000次迭代，成本值为：0.2684918873282239\n",
      "第20000次迭代，成本值为：0.2680916337127301\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "使用正则化，训练集:\n",
      "Accuracy: 0.9383886255924171\n",
      "使用正则化，测试集:\n",
      "Accuracy: 0.93\n"
     ]
    }
   ],
   "source": [
    "parameters = model(train_X, train_Y, lambd=0.7,is_plot=True)\n",
    "print(\"使用正则化，训练集:\")\n",
    "predictions_train = reg_utils.predict(train_X, train_Y, parameters)\n",
    "print(\"使用正则化，测试集:\")\n",
    "predictions_test = reg_utils.predict(test_X, test_Y, parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Model with L2-regularization\")\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-0.75,0.40])\n",
    "axes.set_ylim([-0.75,0.65])\n",
    "reg_utils.plot_decision_boundary(lambda x: reg_utils.predict_dec(parameters, x.T), train_X, train_Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 随机删除节点："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def forward_propagation_with_dropout(X,parameters,keep_prob=0.5):\n",
    "    \"\"\"\n",
    "    实现具有随机舍弃节点的前向传播。\n",
    "    LINEAR -> RELU + DROPOUT -> LINEAR -> RELU + DROPOUT -> LINEAR -> SIGMOID.\n",
    "    \n",
    "    参数：\n",
    "        X  - 输入数据集，维度为（2，示例数）\n",
    "        parameters - 包含参数“W1”，“b1”，“W2”，“b2”，“W3”，“b3”的python字典：\n",
    "            W1  - 权重矩阵，维度为（20,2）\n",
    "            b1  - 偏向量，维度为（20,1）\n",
    "            W2  - 权重矩阵，维度为（3,20）\n",
    "            b2  - 偏向量，维度为（3,1）\n",
    "            W3  - 权重矩阵，维度为（1,3）\n",
    "            b3  - 偏向量，维度为（1,1）\n",
    "        keep_prob  - 随机删除的概率，实数\n",
    "    返回：\n",
    "        A3  - 最后的激活值，维度为（1,1），正向传播的输出\n",
    "        cache - 存储了一些用于计算反向传播的数值的元组\n",
    "    \"\"\"\n",
    "    np.random.seed(1)\n",
    "    \n",
    "    W1 = parameters[\"W1\"]\n",
    "    b1 = parameters[\"b1\"]\n",
    "    W2 = parameters[\"W2\"]\n",
    "    b2 = parameters[\"b2\"]\n",
    "    W3 = parameters[\"W3\"]\n",
    "    b3 = parameters[\"b3\"]\n",
    "    \n",
    "    #LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SIGMOID\n",
    "    Z1 = np.dot(W1,X) + b1\n",
    "    A1 = reg_utils.relu(Z1)\n",
    "    \n",
    "    #下面的步骤1-4对应于上述的步骤1-4。\n",
    "    D1 = np.random.rand(A1.shape[0],A1.shape[1])    #步骤1：初始化矩阵D1 = np.random.rand(..., ...)\n",
    "    D1 = D1 < keep_prob                             #步骤2：将D1的值转换为0或1（使​​用keep_prob作为阈值）\n",
    "    A1 = A1 * D1                                    #步骤3：舍弃A1的一些节点（将它的值变为0或False）\n",
    "    A1 = A1 / keep_prob                             #步骤4：缩放未舍弃的节点(不为0)的值\n",
    "    \"\"\"\n",
    "    #不理解的同学运行一下下面代码就知道了。\n",
    "    import numpy as np\n",
    "    np.random.seed(1)\n",
    "    A1 = np.random.randn(1,3)\n",
    "    \n",
    "    D1 = np.random.rand(A1.shape[0],A1.shape[1])\n",
    "    keep_prob=0.5\n",
    "    D1 = D1 < keep_prob\n",
    "    print(D1)\n",
    "    \n",
    "    A1 = 0.01\n",
    "    A1 = A1 * D1\n",
    "    A1 = A1 / keep_prob\n",
    "    print(A1)\n",
    "    \"\"\"\n",
    "    \n",
    "    Z2 = np.dot(W2,A1) + b2\n",
    "    A2 = reg_utils.relu(Z2)\n",
    "    \n",
    "    #下面的步骤1-4对应于上述的步骤1-4。\n",
    "    D2 = np.random.rand(A2.shape[0],A2.shape[1])    #步骤1：初始化矩阵D2 = np.random.rand(..., ...)\n",
    "    D2 = D2 < keep_prob                             #步骤2：将D2的值转换为0或1（使​​用keep_prob作为阈值）\n",
    "    A2 = A2 * D2                                    #步骤3：舍弃A1的一些节点（将它的值变为0或False）\n",
    "    A2 = A2 / keep_prob                             #步骤4：缩放未舍弃的节点(不为0)的值\n",
    "    \n",
    "    Z3 = np.dot(W3, A2) + b3\n",
    "    A3 = reg_utils.sigmoid(Z3)\n",
    "    \n",
    "    cache = (Z1, D1, A1, W1, b1, Z2, D2, A2, W2, b2, Z3, A3, W3, b3)\n",
    "    \n",
    "    return A3, cache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def backward_propagation_with_dropout(X,Y,cache,keep_prob):\n",
    "    \"\"\"\n",
    "    实现我们随机删除的模型的后向传播。\n",
    "    参数：\n",
    "        X  - 输入数据集，维度为（2，示例数）\n",
    "        Y  - 标签，维度为（输出节点数量，示例数量）\n",
    "        cache - 来自forward_propagation_with_dropout（）的cache输出\n",
    "        keep_prob  - 随机删除的概率，实数\n",
    "    \n",
    "    返回：\n",
    "        gradients - 一个关于每个参数、激活值和预激活变量的梯度值的字典\n",
    "    \"\"\"\n",
    "    m = X.shape[1]\n",
    "    (Z1, D1, A1, W1, b1, Z2, D2, A2, W2, b2, Z3, A3, W3, b3) = cache\n",
    "    \n",
    "    dZ3 = A3 - Y\n",
    "    dW3 = (1 / m) * np.dot(dZ3,A2.T)\n",
    "    db3 = 1. / m * np.sum(dZ3, axis=1, keepdims=True)\n",
    "    dA2 = np.dot(W3.T, dZ3)\n",
    "    \n",
    "    dA2 = dA2 * D2          # 步骤1：使用正向传播期间相同的节点，舍弃那些关闭的节点（因为任何数乘以0或者False都为0或者False）\n",
    "    dA2 = dA2 / keep_prob   # 步骤2：缩放未舍弃的节点(不为0)的值\n",
    "    \n",
    "    dZ2 = np.multiply(dA2, np.int64(A2 > 0))\n",
    "    dW2 = 1. / m * np.dot(dZ2, A1.T)\n",
    "    db2 = 1. / m * np.sum(dZ2, axis=1, keepdims=True)\n",
    "    \n",
    "    dA1 = np.dot(W2.T, dZ2)\n",
    "    \n",
    "    dA1 = dA1 * D1          # 步骤1：使用正向传播期间相同的节点，舍弃那些关闭的节点（因为任何数乘以0或者False都为0或者False）\n",
    "    dA1 = dA1 / keep_prob   # 步骤2：缩放未舍弃的节点(不为0)的值\n",
    "\n",
    "    dZ1 = np.multiply(dA1, np.int64(A1 > 0))\n",
    "    dW1 = 1. / m * np.dot(dZ1, X.T)\n",
    "    db1 = 1. / m * np.sum(dZ1, axis=1, keepdims=True)\n",
    "    \n",
    "    gradients = {\"dZ3\": dZ3, \"dW3\": dW3, \"db3\": db3,\"dA2\": dA2,\n",
    "                 \"dZ2\": dZ2, \"dW2\": dW2, \"db2\": db2, \"dA1\": dA1, \n",
    "                 \"dZ1\": dZ1, \"dW1\": dW1, \"db1\": db1}\n",
    "    \n",
    "    return gradients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第0次迭代，成本值为：0.6543912405149825\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xieyipeng/Documents/MachineLearning/wuenda_py/course2/do_not_src/course2_week1_2/reg_utils.py:121: RuntimeWarning: divide by zero encountered in log\n",
      "  logprobs = np.multiply(-np.log(a3),Y) + np.multiply(-np.log(1 - a3), 1 - Y)\n",
      "/home/xieyipeng/Documents/MachineLearning/wuenda_py/course2/do_not_src/course2_week1_2/reg_utils.py:121: RuntimeWarning: invalid value encountered in multiply\n",
      "  logprobs = np.multiply(-np.log(a3),Y) + np.multiply(-np.log(1 - a3), 1 - Y)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第10000次迭代，成本值为：0.06101698657490562\n",
      "第20000次迭代，成本值为：0.060582435798513114\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "使用随机删除节点，训练集:\n",
      "Accuracy: 0.9289099526066351\n",
      "使用随机删除节点，测试集:\n",
      "Accuracy: 0.95\n"
     ]
    }
   ],
   "source": [
    "parameters = model(train_X, train_Y, keep_prob=0.86, learning_rate=0.3,is_plot=True)\n",
    "\n",
    "print(\"使用随机删除节点，训练集:\")\n",
    "predictions_train = reg_utils.predict(train_X, train_Y, parameters)\n",
    "print(\"使用随机删除节点，测试集:\")\n",
    "reg_utils.predictions_test = reg_utils.predict(test_X, test_Y, parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Model with dropout\")\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-0.75, 0.40])\n",
    "axes.set_ylim([-0.75, 0.65])\n",
    "reg_utils.plot_decision_boundary(lambda x: reg_utils.predict_dec(parameters, x.T), train_X, train_Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 梯度校验："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def forward_propagation(x,theta):\n",
    "    \"\"\"\n",
    "    \n",
    "    实现图中呈现的线性前向传播（计算J）（J（theta）= theta * x）\n",
    "    \n",
    "    参数：\n",
    "    x  - 一个实值输入\n",
    "    theta  - 参数，也是一个实数\n",
    "    \n",
    "    返回：\n",
    "    J  - 函数J的值，用公式J（theta）= theta * x计算\n",
    "    \"\"\"\n",
    "    J = np.dot(theta,x)\n",
    "    \n",
    "    return J"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------测试forward_propagation-----------------\n",
      "J = 8\n"
     ]
    }
   ],
   "source": [
    "#测试forward_propagation\n",
    "print(\"-----------------测试forward_propagation-----------------\")\n",
    "x, theta = 2, 4\n",
    "J = forward_propagation(x, theta)\n",
    "print (\"J = \" + str(J))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def backward_propagation(x,theta):\n",
    "    \"\"\"\n",
    "    计算J相对于θ的导数。\n",
    "    \n",
    "    参数：\n",
    "        x  - 一个实值输入\n",
    "        theta  - 参数，也是一个实数\n",
    "    \n",
    "    返回：\n",
    "        dtheta  - 相对于θ的成本梯度\n",
    "    \"\"\"\n",
    "    dtheta = x\n",
    "    \n",
    "    return dtheta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------测试backward_propagation-----------------\n",
      "dtheta = 2\n"
     ]
    }
   ],
   "source": [
    "#测试backward_propagation\n",
    "print(\"-----------------测试backward_propagation-----------------\")\n",
    "x, theta = 2, 4\n",
    "dtheta = backward_propagation(x, theta)\n",
    "print (\"dtheta = \" + str(dtheta))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient_check(x,theta,epsilon=1e-7):\n",
    "    \"\"\"\n",
    "    \n",
    "    实现图中的反向传播。\n",
    "    \n",
    "    参数：\n",
    "        x  - 一个实值输入\n",
    "        theta  - 参数，也是一个实数\n",
    "        epsilon  - 使用公式（3）计算输入的微小偏移以计算近似梯度\n",
    "    \n",
    "    返回：\n",
    "        近似梯度和后向传播梯度之间的差异\n",
    "    \"\"\"\n",
    "    \n",
    "    #使用公式（3）的左侧计算gradapprox。\n",
    "    thetaplus = theta + epsilon                               # Step 1\n",
    "    thetaminus = theta - epsilon                              # Step 2\n",
    "    J_plus = forward_propagation(x, thetaplus)                # Step 3\n",
    "    J_minus = forward_propagation(x, thetaminus)              # Step 4\n",
    "    gradapprox = (J_plus - J_minus) / (2 * epsilon)           # Step 5\n",
    "    \n",
    "    \n",
    "    #检查gradapprox是否足够接近backward_propagation（）的输出\n",
    "    grad = backward_propagation(x, theta)\n",
    "    \n",
    "    numerator = np.linalg.norm(grad - gradapprox)                      # Step 1'\n",
    "    denominator = np.linalg.norm(grad) + np.linalg.norm(gradapprox)    # Step 2'\n",
    "    difference = numerator / denominator                               # Step 3'\n",
    "    \n",
    "    if difference < 1e-7:\n",
    "        print(\"梯度检查：梯度正常!\")\n",
    "    else:\n",
    "        print(\"梯度检查：梯度超出阈值!\")\n",
    "    \n",
    "    return difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------测试gradient_check-----------------\n",
      "梯度检查：梯度正常!\n",
      "difference = 2.919335883291695e-10\n"
     ]
    }
   ],
   "source": [
    "#测试gradient_check\n",
    "print(\"-----------------测试gradient_check-----------------\")\n",
    "x, theta = 2, 4\n",
    "difference = gradient_check(x, theta)\n",
    "print(\"difference = \" + str(difference))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def forward_propagation_n(X,Y,parameters):\n",
    "    \"\"\"\n",
    "    实现图中的前向传播（并计算成本）。\n",
    "    \n",
    "    参数：\n",
    "        X - 训练集为m个例子\n",
    "        Y -  m个示例的标签\n",
    "        parameters - 包含参数“W1”，“b1”，“W2”，“b2”，“W3”，“b3”的python字典：\n",
    "            W1  - 权重矩阵，维度为（5,4）\n",
    "            b1  - 偏向量，维度为（5,1）\n",
    "            W2  - 权重矩阵，维度为（3,5）\n",
    "            b2  - 偏向量，维度为（3,1）\n",
    "            W3  - 权重矩阵，维度为（1,3）\n",
    "            b3  - 偏向量，维度为（1,1）\n",
    "   \n",
    "    返回：\n",
    "        cost - 成本函数（logistic）\n",
    "    \"\"\"\n",
    "    m = X.shape[1]\n",
    "    W1 = parameters[\"W1\"]\n",
    "    b1 = parameters[\"b1\"]\n",
    "    W2 = parameters[\"W2\"]\n",
    "    b2 = parameters[\"b2\"]\n",
    "    W3 = parameters[\"W3\"]\n",
    "    b3 = parameters[\"b3\"]\n",
    "    \n",
    "    # LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SIGMOID\n",
    "    Z1 = np.dot(W1,X) + b1\n",
    "    A1 = gc_utils.relu(Z1)\n",
    "    \n",
    "    Z2 = np.dot(W2,A1) + b2\n",
    "    A2 = gc_utils.relu(Z2)\n",
    "    \n",
    "    Z3 = np.dot(W3,A2) + b3\n",
    "    A3 = gc_utils.sigmoid(Z3)\n",
    "    \n",
    "    #计算成本\n",
    "    logprobs = np.multiply(-np.log(A3), Y) + np.multiply(-np.log(1 - A3), 1 - Y)\n",
    "    cost = (1 / m) * np.sum(logprobs)\n",
    "    \n",
    "    cache = (Z1, A1, W1, b1, Z2, A2, W2, b2, Z3, A3, W3, b3)\n",
    "\n",
    "    return cost, cache\n",
    "\n",
    "def backward_propagation_n(X,Y,cache):\n",
    "    \"\"\"\n",
    "    实现图中所示的反向传播。\n",
    "    \n",
    "    参数：\n",
    "        X - 输入数据点（输入节点数量，1）\n",
    "        Y - 标签\n",
    "        cache - 来自forward_propagation_n（）的cache输出\n",
    "    \n",
    "    返回：\n",
    "        gradients - 一个字典，其中包含与每个参数、激活和激活前变量相关的成本梯度。\n",
    "    \"\"\"\n",
    "    m = X.shape[1]\n",
    "    (Z1, A1, W1, b1, Z2, A2, W2, b2, Z3, A3, W3, b3) = cache\n",
    "    \n",
    "    dZ3 = A3 - Y\n",
    "    dW3 = (1. / m) * np.dot(dZ3,A2.T)\n",
    "    dW3 = 1. / m * np.dot(dZ3, A2.T)\n",
    "    db3 = 1. / m * np.sum(dZ3, axis=1, keepdims=True)\n",
    "    \n",
    "    dA2 = np.dot(W3.T, dZ3)\n",
    "    dZ2 = np.multiply(dA2, np.int64(A2 > 0))\n",
    "    #dW2 = 1. / m * np.dot(dZ2, A1.T) * 2  # Should not multiply by 2\n",
    "    dW2 = 1. / m * np.dot(dZ2, A1.T)\n",
    "    db2 = 1. / m * np.sum(dZ2, axis=1, keepdims=True)\n",
    "    \n",
    "    dA1 = np.dot(W2.T, dZ2)\n",
    "    dZ1 = np.multiply(dA1, np.int64(A1 > 0))\n",
    "    dW1 = 1. / m * np.dot(dZ1, X.T)\n",
    "    #db1 = 4. / m * np.sum(dZ1, axis=1, keepdims=True) # Should not multiply by 4\n",
    "    db1 = 1. / m * np.sum(dZ1, axis=1, keepdims=True)\n",
    "    \n",
    "    gradients = {\"dZ3\": dZ3, \"dW3\": dW3, \"db3\": db3,\n",
    "                 \"dA2\": dA2, \"dZ2\": dZ2, \"dW2\": dW2, \"db2\": db2,\n",
    "                 \"dA1\": dA1, \"dZ1\": dZ1, \"dW1\": dW1, \"db1\": db1}\n",
    " \n",
    "    return gradients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient_check_n(parameters,gradients,X,Y,epsilon=1e-7):\n",
    "    \"\"\"\n",
    "    检查backward_propagation_n是否正确计算forward_propagation_n输出的成本梯度\n",
    "    \n",
    "    参数：\n",
    "        parameters - 包含参数“W1”，“b1”，“W2”，“b2”，“W3”，“b3”的python字典：\n",
    "        grad_output_propagation_n的输出包含与参数相关的成本梯度。\n",
    "        x  - 输入数据点，维度为（输入节点数量，1）\n",
    "        y  - 标签\n",
    "        epsilon  - 计算输入的微小偏移以计算近似梯度\n",
    "    \n",
    "    返回：\n",
    "        difference - 近似梯度和后向传播梯度之间的差异\n",
    "    \"\"\"\n",
    "    #初始化参数\n",
    "    parameters_values , keys = gc_utils.dictionary_to_vector(parameters) #keys用不到\n",
    "    grad = gc_utils.gradients_to_vector(gradients)\n",
    "    num_parameters = parameters_values.shape[0]\n",
    "    J_plus = np.zeros((num_parameters,1))\n",
    "    J_minus = np.zeros((num_parameters,1))\n",
    "    gradapprox = np.zeros((num_parameters,1))\n",
    "    \n",
    "    #计算gradapprox\n",
    "    for i in range(num_parameters):\n",
    "        #计算J_plus [i]。输入：“parameters_values，epsilon”。输出=“J_plus [i]”\n",
    "        thetaplus = np.copy(parameters_values)                                                  # Step 1\n",
    "        thetaplus[i][0] = thetaplus[i][0] + epsilon                                             # Step 2\n",
    "        J_plus[i], cache = forward_propagation_n(X,Y,gc_utils.vector_to_dictionary(thetaplus))  # Step 3 ，cache用不到\n",
    "        \n",
    "        #计算J_minus [i]。输入：“parameters_values，epsilon”。输出=“J_minus [i]”。\n",
    "        thetaminus = np.copy(parameters_values)                                                 # Step 1\n",
    "        thetaminus[i][0] = thetaminus[i][0] - epsilon                                           # Step 2        \n",
    "        J_minus[i], cache = forward_propagation_n(X,Y,gc_utils.vector_to_dictionary(thetaminus))# Step 3 ，cache用不到\n",
    "        \n",
    "        #计算gradapprox[i]\n",
    "        gradapprox[i] = (J_plus[i] - J_minus[i]) / (2 * epsilon)\n",
    "        \n",
    "    #通过计算差异比较gradapprox和后向传播梯度。\n",
    "    numerator = np.linalg.norm(grad - gradapprox)                                     # Step 1'\n",
    "    denominator = np.linalg.norm(grad) + np.linalg.norm(gradapprox)                   # Step 2'\n",
    "    difference = numerator / denominator                                              # Step 3'\n",
    "    \n",
    "    if difference < 1e-7:\n",
    "        print(\"梯度检查：梯度正常!\")\n",
    "    else:\n",
    "        print(\"梯度检查：梯度超出阈值!\")\n",
    "    \n",
    "    return difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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